Overview

Dataset statistics

Number of variables29
Number of observations102825
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.8 MiB
Average record size in memory232.0 B

Variable types

Numeric14
Categorical15

Alerts

df_index is uniformly distributed Uniform
df_index has unique values Unique
Departure Delay in Minutes has 58649 (57.0%) zeros Zeros
Arrival Delay in Minutes has 58135 (56.5%) zeros Zeros

Reproduction

Analysis started2023-02-18 17:49:16.437437
Analysis finished2023-02-18 17:50:23.785719
Duration1 minute and 7.35 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct102825
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51941.91747
Minimum0
Maximum103903
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:23.967246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5193.2
Q125968
median51939
Q377911
95-th percentile98703.8
Maximum103903
Range103903
Interquartile range (IQR)51943

Descriptive statistics

Standard deviation29993.15899
Coefficient of variation (CV)0.5774364993
Kurtosis-1.200183173
Mean51941.91747
Median Absolute Deviation (MAD)25972
Skewness0.0001207897413
Sum5340927664
Variance899589586.2
MonotonicityStrictly increasing
2023-02-18T12:50:24.265271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
692631
 
< 0.1%
692741
 
< 0.1%
692731
 
< 0.1%
692721
 
< 0.1%
692711
 
< 0.1%
692701
 
< 0.1%
692691
 
< 0.1%
692681
 
< 0.1%
692671
 
< 0.1%
Other values (102815)102815
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1039031
< 0.1%
1039021
< 0.1%
1039011
< 0.1%
1039001
< 0.1%
1038991
< 0.1%
1038981
< 0.1%
1038971
< 0.1%
1038961
< 0.1%
1038951
< 0.1%
1038941
< 0.1%

Flight Distance
Real number (ℝ≥0)

Distinct3798
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.702518394
Minimum3.465735903
Maximum8.3532615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:24.440939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.465735903
5-th percentile5.170483995
Q16.025865974
median6.735780014
Q37.459338895
95-th percentile8.123557835
Maximum8.3532615
Range4.887525597
Interquartile range (IQR)1.433472921

Descriptive statistics

Standard deviation0.9142733958
Coefficient of variation (CV)0.136407443
Kurtosis-0.7086499204
Mean6.702518394
Median Absolute Deviation (MAD)0.7189399351
Skewness-0.2035253457
Sum689186.4539
Variance0.8358958423
MonotonicityNot monotonic
2023-02-18T12:50:24.601600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.823045895654
 
0.6%
6.388561406395
 
0.4%
6.003887067390
 
0.4%
6.760414691368
 
0.4%
7.814399634361
 
0.4%
6.104793232358
 
0.3%
5.468060141350
 
0.3%
5.991464547330
 
0.3%
5.733341277329
 
0.3%
5.262690189328
 
0.3%
Other values (3788)98962
96.2%
ValueCountFrequency (%)
3.4657359038
 
< 0.1%
4.0430512688
 
< 0.1%
4.219507705124
0.1%
4.30406509358
0.1%
4.31748811430
 
< 0.1%
4.3438054221
 
< 0.1%
4.35670882741
 
< 0.1%
4.36944785230
 
< 0.1%
4.3944491552
 
< 0.1%
4.4188406087
 
< 0.1%
ValueCountFrequency (%)
8.353261517
< 0.1%
8.29429960911
< 0.1%
8.294049645
 
< 0.1%
8.2937996098
< 0.1%
8.2935495159
< 0.1%
8.2932993598
< 0.1%
8.293049146
 
< 0.1%
8.2927988586
 
< 0.1%
8.29254851413
< 0.1%
8.2922981076
 
< 0.1%

Departure Delay in Minutes
Real number (ℝ≥0)

ZEROS

Distinct214
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.192897759
Minimum0
Maximum5.375278408
Zeros58649
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:24.775278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.564949357
95-th percentile4.248495242
Maximum5.375278408
Range5.375278408
Interquartile range (IQR)2.564949357

Descriptive statistics

Standard deviation1.570036356
Coefficient of variation (CV)1.316153328
Kurtosis-0.6816007504
Mean1.192897759
Median Absolute Deviation (MAD)0
Skewness0.8931651586
Sum122659.7121
Variance2.46501416
MonotonicityNot monotonic
2023-02-18T12:50:24.940144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058649
57.0%
0.69314718062947
 
2.9%
1.0986122892272
 
2.2%
1.3862943612007
 
2.0%
1.6094379121852
 
1.8%
1.7917594691692
 
1.6%
1.9459101491515
 
1.5%
2.0794415421392
 
1.4%
2.1972245771295
 
1.3%
2.3025850931254
 
1.2%
Other values (204)27950
27.2%
ValueCountFrequency (%)
058649
57.0%
0.69314718062947
 
2.9%
1.0986122892272
 
2.2%
1.3862943612007
 
2.0%
1.6094379121852
 
1.8%
1.7917594691692
 
1.6%
1.9459101491515
 
1.5%
2.0794415421392
 
1.4%
2.1972245771295
 
1.3%
2.3025850931254
 
1.2%
ValueCountFrequency (%)
5.3752784081
 
< 0.1%
5.3706380282
 
< 0.1%
5.3612921665
< 0.1%
5.3565862752
 
< 0.1%
5.3518581332
 
< 0.1%
5.3471075314
< 0.1%
5.337538081
 
< 0.1%
5.3327187932
 
< 0.1%
5.3278761693
< 0.1%
5.3230099793
< 0.1%

Arrival Delay in Minutes
Real number (ℝ≥0)

ZEROS

Distinct320
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.225234294
Minimum0
Maximum5.493061443
Zeros58135
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:25.114075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.564949357
95-th percentile4.262679877
Maximum5.493061443
Range5.493061443
Interquartile range (IQR)2.564949357

Descriptive statistics

Standard deviation1.583285132
Coefficient of variation (CV)1.292230507
Kurtosis-0.764447712
Mean1.225234294
Median Absolute Deviation (MAD)0
Skewness0.846310121
Sum125984.7162
Variance2.506791809
MonotonicityNot monotonic
2023-02-18T12:50:25.266471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058135
56.5%
0.69314718062211
 
2.2%
1.0986122892061
 
2.0%
1.3862943611952
 
1.9%
1.6094379121906
 
1.9%
1.7917594691657
 
1.6%
1.9459101491616
 
1.6%
2.0794415421481
 
1.4%
2.1972245771394
 
1.4%
2.3025850931264
 
1.2%
Other values (310)29148
28.3%
ValueCountFrequency (%)
058135
56.5%
0.5437442748116
 
0.1%
0.69314718062211
 
2.2%
0.99423822779
 
< 0.1%
1.0986122892061
 
2.0%
1.3036965158
 
< 0.1%
1.3862943611952
 
1.9%
1.5396817523
 
< 0.1%
1.6094379121906
 
1.9%
1.7304737229
 
< 0.1%
ValueCountFrequency (%)
5.4930614431
 
< 0.1%
5.4722706741
 
< 0.1%
5.4380793092
 
< 0.1%
5.4293456291
 
< 0.1%
5.4249500171
 
< 0.1%
5.4205349991
 
< 0.1%
5.4161004023
< 0.1%
5.4071717713
< 0.1%
5.4026773823
< 0.1%
5.3936275466
< 0.1%

Gender_Female
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
1.0
52169 
0.0
50656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.052169
50.7%
0.050656
49.3%

Length

2023-02-18T12:50:25.443632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:25.594906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.052169
50.7%
0.050656
49.3%

Most occurring characters

ValueCountFrequency (%)
0153481
49.8%
.102825
33.3%
152169
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0153481
74.6%
152169
 
25.4%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0153481
49.8%
.102825
33.3%
152169
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0153481
49.8%
.102825
33.3%
152169
 
16.9%

Gender_Male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
0.0
52169 
1.0
50656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.052169
50.7%
1.050656
49.3%

Length

2023-02-18T12:50:25.735796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:25.883820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.052169
50.7%
1.050656
49.3%

Most occurring characters

ValueCountFrequency (%)
0154994
50.2%
.102825
33.3%
150656
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0154994
75.4%
150656
 
24.6%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0154994
50.2%
.102825
33.3%
150656
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0154994
50.2%
.102825
33.3%
150656
 
16.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
1.0
84003 
0.0
18822 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.084003
81.7%
0.018822
 
18.3%

Length

2023-02-18T12:50:26.012848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:26.199706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.084003
81.7%
0.018822
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0121647
39.4%
.102825
33.3%
184003
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0121647
59.2%
184003
40.8%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0121647
39.4%
.102825
33.3%
184003
27.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0121647
39.4%
.102825
33.3%
184003
27.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
0.0
84003 
1.0
18822 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.084003
81.7%
1.018822
 
18.3%

Length

2023-02-18T12:50:26.338048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:26.486360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.084003
81.7%
1.018822
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0186828
60.6%
.102825
33.3%
118822
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0186828
90.8%
118822
 
9.2%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0186828
60.6%
.102825
33.3%
118822
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0186828
60.6%
.102825
33.3%
118822
 
6.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
1.0
70897 
0.0
31928 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.070897
68.9%
0.031928
31.1%

Length

2023-02-18T12:50:26.627687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:26.772463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.070897
68.9%
0.031928
31.1%

Most occurring characters

ValueCountFrequency (%)
0134753
43.7%
.102825
33.3%
170897
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0134753
65.5%
170897
34.5%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0134753
43.7%
.102825
33.3%
170897
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0134753
43.7%
.102825
33.3%
170897
23.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
0.0
70897 
1.0
31928 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.070897
68.9%
1.031928
31.1%

Length

2023-02-18T12:50:26.901117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:27.047239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.070897
68.9%
1.031928
31.1%

Most occurring characters

ValueCountFrequency (%)
0173722
56.3%
.102825
33.3%
131928
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0173722
84.5%
131928
 
15.5%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0173722
56.3%
.102825
33.3%
131928
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0173722
56.3%
.102825
33.3%
131928
 
10.4%

Class_Business
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
0.0
53700 
1.0
49125 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.053700
52.2%
1.049125
47.8%

Length

2023-02-18T12:50:27.162180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:27.279818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.053700
52.2%
1.049125
47.8%

Most occurring characters

ValueCountFrequency (%)
0156525
50.7%
.102825
33.3%
149125
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0156525
76.1%
149125
 
23.9%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0156525
50.7%
.102825
33.3%
149125
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0156525
50.7%
.102825
33.3%
149125
 
15.9%

Class_Eco
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
0.0
56547 
1.0
46278 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.056547
55.0%
1.046278
45.0%

Length

2023-02-18T12:50:27.380430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:27.495047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.056547
55.0%
1.046278
45.0%

Most occurring characters

ValueCountFrequency (%)
0159372
51.7%
.102825
33.3%
146278
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0159372
77.5%
146278
 
22.5%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0159372
51.7%
.102825
33.3%
146278
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0159372
51.7%
.102825
33.3%
146278
 
15.0%

Class_Eco Plus
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
0.0
95403 
1.0
 
7422

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.095403
92.8%
1.07422
 
7.2%

Length

2023-02-18T12:50:27.602248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:27.719759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.095403
92.8%
1.07422
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0198228
64.3%
.102825
33.3%
17422
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0198228
96.4%
17422
 
3.6%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0198228
64.3%
.102825
33.3%
17422
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0198228
64.3%
.102825
33.3%
17422
 
2.4%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.160915921 × 10-16
Minimum-2.143738423
Maximum3.020779545
Zeros0
Zeros (%)0.0%
Negative50869
Negative (%)49.5%
Memory size803.4 KiB
2023-02-18T12:50:27.840305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2.143738423
5-th percentile-1.680256041
Q1-0.8195030465
median0.04124994807
Q30.769579405
95-th percentile1.6303324
Maximum3.020779545
Range5.164517967
Interquartile range (IQR)1.589082451

Descriptive statistics

Standard deviation1.000004863
Coefficient of variation (CV)8.613930125 × 1015
Kurtosis-0.7198928334
Mean1.160915921 × 10-16
Median Absolute Deviation (MAD)0.7945412257
Skewness-0.004753400847
Sum8.185452316 × 10-12
Variance1.000009725
MonotonicityNot monotonic
2023-02-18T12:50:28.003331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.024961820742938
 
2.9%
-0.95192658412769
 
2.7%
0.041249948072544
 
2.5%
0.30609702332462
 
2.4%
0.17367348572431
 
2.4%
0.10746171692427
 
2.4%
-1.1505618912338
 
2.3%
0.37230879212319
 
2.3%
-1.0843501222318
 
2.3%
0.50473232972309
 
2.2%
Other values (65)77970
75.8%
ValueCountFrequency (%)
-2.143738423557
0.5%
-2.077526654634
0.6%
-2.011314885682
0.7%
-1.945103116670
0.7%
-1.878891347667
0.6%
-1.812679579630
0.6%
-1.74646781619
0.6%
-1.680256041701
0.7%
-1.614044272807
0.8%
-1.547832503884
0.9%
ValueCountFrequency (%)
3.02077954517
 
< 0.1%
2.689720775
 
0.1%
2.62350893240
 
< 0.1%
2.55729716330
 
< 0.1%
2.49108539485
0.1%
2.42487362545
 
< 0.1%
2.35866185660
 
0.1%
2.29245008844
 
< 0.1%
2.22623831949
 
< 0.1%
2.16002655198
0.2%

Inflight wifi service
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.81099149
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:28.479494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.249109608
Coefficient of variation (CV)0.4443662005
Kurtosis-0.9743817837
Mean2.81099149
Median Absolute Deviation (MAD)1
Skewness0.1658864987
Sum289040.2
Variance1.560274813
MonotonicityNot monotonic
2023-02-18T12:50:28.610402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
325904
25.2%
225769
25.1%
419764
19.2%
117835
17.3%
511408
11.1%
2.6285
 
0.3%
2.8277
 
0.3%
2.4231
 
0.2%
3.2217
 
0.2%
2.2181
 
0.2%
Other values (11)954
 
0.9%
ValueCountFrequency (%)
117835
17.3%
1.284
 
0.1%
1.496
 
0.1%
1.695
 
0.1%
1.8135
 
0.1%
225769
25.1%
2.2181
 
0.2%
2.4231
 
0.2%
2.6285
 
0.3%
2.8277
 
0.3%
ValueCountFrequency (%)
511408
11.1%
4.821
 
< 0.1%
4.628
 
< 0.1%
4.451
 
< 0.1%
4.237
 
< 0.1%
419764
19.2%
3.8111
 
0.1%
3.6139
 
0.1%
3.4157
 
0.2%
3.2217
 
0.2%
Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.239708242
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:28.750291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.366744974
Coefficient of variation (CV)0.4218728576
Kurtosis-1.17047319
Mean3.239708242
Median Absolute Deviation (MAD)1
Skewness-0.272600001
Sum333123
Variance1.867991823
MonotonicityNot monotonic
2023-02-18T12:50:28.874778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
425782
25.1%
522288
21.7%
318111
17.6%
217151
16.7%
115403
15.0%
4.2530
 
0.5%
4.4479
 
0.5%
3.8424
 
0.4%
3.6373
 
0.4%
3.2346
 
0.3%
Other values (11)1938
 
1.9%
ValueCountFrequency (%)
115403
15.0%
1.228
 
< 0.1%
1.429
 
< 0.1%
1.659
 
0.1%
1.899
 
0.1%
217151
16.7%
2.2151
 
0.1%
2.4209
 
0.2%
2.6248
 
0.2%
2.8263
 
0.3%
ValueCountFrequency (%)
522288
21.7%
4.8198
 
0.2%
4.6322
 
0.3%
4.4479
 
0.5%
4.2530
 
0.5%
425782
25.1%
3.8424
 
0.4%
3.6373
 
0.4%
3.4332
 
0.3%
3.2346
 
0.3%

Ease of Online booking
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.872089472
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:29.012801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.285913443
Coefficient of variation (CV)0.4477275014
Kurtosis-1.050726739
Mean2.872089472
Median Absolute Deviation (MAD)1
Skewness0.1294870565
Sum295322.6
Variance1.653573383
MonotonicityNot monotonic
2023-02-18T12:50:29.149429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
324594
23.9%
224063
23.4%
419558
19.0%
117553
17.1%
513753
13.4%
2.8368
 
0.4%
2.6362
 
0.4%
3.2325
 
0.3%
2.4320
 
0.3%
2.2273
 
0.3%
Other values (11)1656
 
1.6%
ValueCountFrequency (%)
117553
17.1%
1.2191
 
0.2%
1.4212
 
0.2%
1.6182
 
0.2%
1.8198
 
0.2%
224063
23.4%
2.2273
 
0.3%
2.4320
 
0.3%
2.6362
 
0.4%
2.8368
 
0.4%
ValueCountFrequency (%)
513753
13.4%
4.823
 
< 0.1%
4.646
 
< 0.1%
4.461
 
0.1%
4.293
 
0.1%
419558
19.0%
3.8198
 
0.2%
3.6203
 
0.2%
3.4249
 
0.2%
3.2325
 
0.3%

Gate location
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.976108923
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:29.276619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.27777806
Coefficient of variation (CV)0.429345193
Kurtosis-1.030922575
Mean2.976108923
Median Absolute Deviation (MAD)1
Skewness-0.05797733329
Sum306018.4
Variance1.63271677
MonotonicityNot monotonic
2023-02-18T12:50:29.382224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
328270
27.5%
424153
23.5%
219272
18.7%
117399
16.9%
513730
13.4%
3.41
 
< 0.1%
ValueCountFrequency (%)
117399
16.9%
219272
18.7%
328270
27.5%
3.41
 
< 0.1%
424153
23.5%
513730
13.4%
ValueCountFrequency (%)
513730
13.4%
424153
23.5%
3.41
 
< 0.1%
328270
27.5%
219272
18.7%
117399
16.9%

Food and drink
Real number (ℝ≥0)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.206220277
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:29.510569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.326409817
Coefficient of variation (CV)0.4136989049
Kurtosis-1.158902516
Mean3.206220277
Median Absolute Deviation (MAD)1
Skewness-0.1457301453
Sum329679.6
Variance1.759363004
MonotonicityNot monotonic
2023-02-18T12:50:29.638201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
424088
23.4%
522144
21.5%
322076
21.5%
221759
21.2%
112701
12.4%
2.68
 
< 0.1%
3.28
 
< 0.1%
2.48
 
< 0.1%
1.25
 
< 0.1%
2.25
 
< 0.1%
Other values (10)23
 
< 0.1%
ValueCountFrequency (%)
112701
12.4%
1.25
 
< 0.1%
1.41
 
< 0.1%
1.62
 
< 0.1%
1.81
 
< 0.1%
221759
21.2%
2.25
 
< 0.1%
2.48
 
< 0.1%
2.68
 
< 0.1%
2.84
 
< 0.1%
ValueCountFrequency (%)
522144
21.5%
4.63
 
< 0.1%
4.43
 
< 0.1%
4.21
 
< 0.1%
424088
23.4%
3.84
 
< 0.1%
3.62
 
< 0.1%
3.42
 
< 0.1%
3.28
 
< 0.1%
322076
21.5%

Online boarding
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.313075614
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:29.774717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.262961393
Coefficient of variation (CV)0.3812051218
Kurtosis-0.954591248
Mean3.313075614
Median Absolute Deviation (MAD)0.6
Skewness-0.3215895344
Sum340667
Variance1.59507148
MonotonicityNot monotonic
2023-02-18T12:50:29.899484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
430547
29.7%
321837
21.2%
520479
19.9%
217483
17.0%
110629
 
10.3%
2.6210
 
0.2%
2.8192
 
0.2%
2.4188
 
0.2%
3.2170
 
0.2%
3.4170
 
0.2%
Other values (11)920
 
0.9%
ValueCountFrequency (%)
110629
10.3%
1.2122
 
0.1%
1.4117
 
0.1%
1.694
 
0.1%
1.8105
 
0.1%
217483
17.0%
2.2134
 
0.1%
2.4188
 
0.2%
2.6210
 
0.2%
2.8192
 
0.2%
ValueCountFrequency (%)
520479
19.9%
4.816
 
< 0.1%
4.627
 
< 0.1%
4.431
 
< 0.1%
4.261
 
0.1%
430547
29.7%
3.882
 
0.1%
3.6131
 
0.1%
3.4170
 
0.2%
3.2170
 
0.2%

Seat comfort
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4.0
31470 
5.0
26214 
3.0
18497 
2.0
14721 
1.0
11923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
4.031470
30.6%
5.026214
25.5%
3.018497
18.0%
2.014721
14.3%
1.011923
 
11.6%

Length

2023-02-18T12:50:30.032423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:30.170144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.031470
30.6%
5.026214
25.5%
3.018497
18.0%
2.014721
14.3%
1.011923
 
11.6%

Most occurring characters

ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
431470
 
10.2%
526214
 
8.5%
318497
 
6.0%
214721
 
4.8%
111923
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0102825
50.0%
431470
 
15.3%
526214
 
12.7%
318497
 
9.0%
214721
 
7.2%
111923
 
5.8%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
431470
 
10.2%
526214
 
8.5%
318497
 
6.0%
214721
 
4.8%
111923
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
431470
 
10.2%
526214
 
8.5%
318497
 
6.0%
214721
 
4.8%
111923
 
3.9%

Inflight entertainment
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.360132264
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:30.295215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.333051696
Coefficient of variation (CV)0.3967259594
Kurtosis-1.06370032
Mean3.360132264
Median Absolute Deviation (MAD)1
Skewness-0.3656678073
Sum345505.6
Variance1.777026824
MonotonicityNot monotonic
2023-02-18T12:50:30.414158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
429144
28.3%
525021
24.3%
318831
18.3%
217486
17.0%
112334
12.0%
2.82
 
< 0.1%
3.82
 
< 0.1%
3.61
 
< 0.1%
3.41
 
< 0.1%
2.41
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
112334
12.0%
1.21
 
< 0.1%
1.81
 
< 0.1%
217486
17.0%
2.41
 
< 0.1%
2.82
 
< 0.1%
318831
18.3%
3.41
 
< 0.1%
3.61
 
< 0.1%
3.82
 
< 0.1%
ValueCountFrequency (%)
525021
24.3%
429144
28.3%
3.82
 
< 0.1%
3.61
 
< 0.1%
3.41
 
< 0.1%
318831
18.3%
2.82
 
< 0.1%
2.41
 
< 0.1%
217486
17.0%
1.81
 
< 0.1%

On-board service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4.0
30637 
5.0
23425 
3.0
22579 
2.0
14448 
1.0
11736 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.030637
29.8%
5.023425
22.8%
3.022579
22.0%
2.014448
14.1%
1.011736
 
11.4%

Length

2023-02-18T12:50:30.550238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:30.689033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.030637
29.8%
5.023425
22.8%
3.022579
22.0%
2.014448
14.1%
1.011736
 
11.4%

Most occurring characters

ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
430637
 
9.9%
523425
 
7.6%
322579
 
7.3%
214448
 
4.7%
111736
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0102825
50.0%
430637
 
14.9%
523425
 
11.4%
322579
 
11.0%
214448
 
7.0%
111736
 
5.7%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
430637
 
9.9%
523425
 
7.6%
322579
 
7.3%
214448
 
4.7%
111736
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
430637
 
9.9%
523425
 
7.6%
322579
 
7.3%
214448
 
4.7%
111736
 
3.8%

Leg room service
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.359751033
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:30.818612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.300037201
Coefficient of variation (CV)0.3869445052
Kurtosis-1.060894858
Mean3.359751033
Median Absolute Deviation (MAD)1
Skewness-0.3140575415
Sum345466.4
Variance1.690096725
MonotonicityNot monotonic
2023-02-18T12:50:30.948028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
428406
27.6%
524406
23.7%
319875
19.3%
219459
18.9%
110320
 
10.0%
2.438
 
< 0.1%
2.637
 
< 0.1%
2.237
 
< 0.1%
2.834
 
< 0.1%
1.833
 
< 0.1%
Other values (11)180
 
0.2%
ValueCountFrequency (%)
110320
10.0%
1.217
 
< 0.1%
1.422
 
< 0.1%
1.621
 
< 0.1%
1.833
 
< 0.1%
219459
18.9%
2.237
 
< 0.1%
2.438
 
< 0.1%
2.637
 
< 0.1%
2.834
 
< 0.1%
ValueCountFrequency (%)
524406
23.7%
4.84
 
< 0.1%
4.63
 
< 0.1%
4.46
 
< 0.1%
4.211
 
< 0.1%
428406
27.6%
3.824
 
< 0.1%
3.620
 
< 0.1%
3.430
 
< 0.1%
3.222
 
< 0.1%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4.0
36962 
5.0
26849 
3.0
20401 
2.0
11420 
1.0
7193 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.036962
35.9%
5.026849
26.1%
3.020401
19.8%
2.011420
 
11.1%
1.07193
 
7.0%

Length

2023-02-18T12:50:31.092917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:31.228622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.036962
35.9%
5.026849
26.1%
3.020401
19.8%
2.011420
 
11.1%
1.07193
 
7.0%

Most occurring characters

ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
436962
 
12.0%
526849
 
8.7%
320401
 
6.6%
211420
 
3.7%
17193
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0102825
50.0%
436962
 
18.0%
526849
 
13.1%
320401
 
9.9%
211420
 
5.6%
17193
 
3.5%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
436962
 
12.0%
526849
 
8.7%
320401
 
6.6%
211420
 
3.7%
17193
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
436962
 
12.0%
526849
 
8.7%
320401
 
6.6%
211420
 
3.7%
17193
 
2.3%

Checkin service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4.0
28761 
3.0
28248 
5.0
20362 
1.0
12737 
2.0
12717 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.028761
28.0%
3.028248
27.5%
5.020362
19.8%
1.012737
12.4%
2.012717
12.4%

Length

2023-02-18T12:50:31.351875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:31.485043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.028761
28.0%
3.028248
27.5%
5.020362
19.8%
1.012737
12.4%
2.012717
12.4%

Most occurring characters

ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
428761
 
9.3%
328248
 
9.2%
520362
 
6.6%
112737
 
4.1%
212717
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0102825
50.0%
428761
 
14.0%
328248
 
13.7%
520362
 
9.9%
112737
 
6.2%
212717
 
6.2%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
428761
 
9.3%
328248
 
9.2%
520362
 
6.6%
112737
 
4.1%
212717
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
428761
 
9.3%
328248
 
9.2%
520362
 
6.6%
112737
 
4.1%
212717
 
4.1%

Inflight service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
4.0
37676 
5.0
26922 
3.0
19977 
2.0
11314 
1.0
6936 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308475
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.037676
36.6%
5.026922
26.2%
3.019977
19.4%
2.011314
 
11.0%
1.06936
 
6.7%

Length

2023-02-18T12:50:31.619639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:31.747173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.037676
36.6%
5.026922
26.2%
3.019977
19.4%
2.011314
 
11.0%
1.06936
 
6.7%

Most occurring characters

ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
437676
 
12.2%
526922
 
8.7%
319977
 
6.5%
211314
 
3.7%
16936
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number205650
66.7%
Other Punctuation102825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0102825
50.0%
437676
 
18.3%
526922
 
13.1%
319977
 
9.7%
211314
 
5.5%
16936
 
3.4%
Other Punctuation
ValueCountFrequency (%)
.102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
437676
 
12.2%
526922
 
8.7%
319977
 
6.5%
211314
 
3.7%
16936
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII308475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.102825
33.3%
0102825
33.3%
437676
 
12.2%
526922
 
8.7%
319977
 
6.5%
211314
 
3.7%
16936
 
2.2%

Cleanliness
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.286469244
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2023-02-18T12:50:31.865863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.311284744
Coefficient of variation (CV)0.3989949841
Kurtosis-1.012822168
Mean3.286469244
Median Absolute Deviation (MAD)1
Skewness-0.2989911181
Sum337931.2
Variance1.719467679
MonotonicityNot monotonic
2023-02-18T12:50:31.979495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
426885
26.1%
324377
23.7%
522432
21.8%
215956
15.5%
113166
12.8%
2.42
 
< 0.1%
3.22
 
< 0.1%
2.81
 
< 0.1%
3.61
 
< 0.1%
1.61
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
113166
12.8%
1.21
 
< 0.1%
1.61
 
< 0.1%
1.81
 
< 0.1%
215956
15.5%
2.42
 
< 0.1%
2.81
 
< 0.1%
324377
23.7%
3.22
 
< 0.1%
3.61
 
< 0.1%
ValueCountFrequency (%)
522432
21.8%
426885
26.1%
3.61
 
< 0.1%
3.22
 
< 0.1%
324377
23.7%
2.81
 
< 0.1%
2.42
 
< 0.1%
215956
15.5%
1.81
 
< 0.1%
1.61
 
< 0.1%

satisfaction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size803.4 KiB
neutral or dissatisfied
58226 
satisfied
44599 

Length

Max length23
Median length23
Mean length16.92768296
Min length9

Characters and Unicode

Total characters1740589
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowneutral or dissatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied58226
56.6%
satisfied44599
43.4%

Length

2023-02-18T12:50:32.128469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-18T12:50:32.251717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
neutral58226
26.6%
or58226
26.6%
dissatisfied58226
26.6%
satisfied44599
20.3%

Most occurring characters

ValueCountFrequency (%)
i263876
15.2%
s263876
15.2%
e161051
9.3%
t161051
9.3%
a161051
9.3%
d161051
9.3%
r116452
6.7%
116452
6.7%
f102825
 
5.9%
n58226
 
3.3%
Other values (3)174678
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1624137
93.3%
Space Separator116452
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i263876
16.2%
s263876
16.2%
e161051
9.9%
t161051
9.9%
a161051
9.9%
d161051
9.9%
r116452
7.2%
f102825
 
6.3%
n58226
 
3.6%
u58226
 
3.6%
Other values (2)116452
7.2%
Space Separator
ValueCountFrequency (%)
116452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1624137
93.3%
Common116452
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i263876
16.2%
s263876
16.2%
e161051
9.9%
t161051
9.9%
a161051
9.9%
d161051
9.9%
r116452
7.2%
f102825
 
6.3%
n58226
 
3.6%
u58226
 
3.6%
Other values (2)116452
7.2%
Common
ValueCountFrequency (%)
116452
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1740589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i263876
15.2%
s263876
15.2%
e161051
9.3%
t161051
9.3%
a161051
9.3%
d161051
9.3%
r116452
6.7%
116452
6.7%
f102825
 
5.9%
n58226
 
3.3%
Other values (3)174678
10.0%

Interactions

2023-02-18T12:50:16.626406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:23.211043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:26.629540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:30.338683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:33.872821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:37.083977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:40.520405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:50.114263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:54.065519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:57.645657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:01.630419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:05.794487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:09.273982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:12.697829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:16.879681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:23.546952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:26.862405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:30.556150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:34.092444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:37.289850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:40.820777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:50.350486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:54.301390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:57.882217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:01.907064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:06.051609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:09.498139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:13.175689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:17.131184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:23.765308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:27.134887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:30.760497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:34.297320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:37.484231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:41.071401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:50.592102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:54.534655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:58.125505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:02.205999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:06.284865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:09.724988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:13.397258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:17.373894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:23.950258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:27.360412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:30.984311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:34.490536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:37.690798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:41.360231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:50.841012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:54.800700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:58.351182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:02.457273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:06.513878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:09.952305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:13.629448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:17.627479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:24.135575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:27.573939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:31.219947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:34.702079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:37.872324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:41.613192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:51.080287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:55.027748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:58.581101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:02.715362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:06.796053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:10.186572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:13.849488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:17.863809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:24.325813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:27.789049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:31.516007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:34.913552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:38.087385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:41.871283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:51.350474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:55.278236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:58.817625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:02.952234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:07.043546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:10.435228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:14.086970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:18.134770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:24.540637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:28.049639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:31.828131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:35.178936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:38.320272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:47.594861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:51.595085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:55.551006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:59.308472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:03.256319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:07.306568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:10.698207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:14.354007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:18.391452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:24.761431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:28.342458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:32.108096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:35.420839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:38.562228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:47.861417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:51.855866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:55.797327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:59.591664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:03.571260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:07.558711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:10.962211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:14.627382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:18.648252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:24.989155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:28.635130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:32.355000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:35.669088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:38.791214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:48.135546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:52.338873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:56.079882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:59.828593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:03.892614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:07.817113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:11.219731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:14.882070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:19.042511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:25.216489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:28.952395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:32.615620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:35.912363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:39.029735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:48.446325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:52.615817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:56.340834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:00.089256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:04.171141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:08.070580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:11.476648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:15.177437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:19.315026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:25.425052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:29.246563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:32.889501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:36.158873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:39.334045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:48.755474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:52.933244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:56.610282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:00.354446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:04.552584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:08.294589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:11.732227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:15.457472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:19.598438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:25.652509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:29.559880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:33.133194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:36.394932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:39.631403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:49.324947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:53.270722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:56.885213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:00.630950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:04.889432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:08.540657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:11.957353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:15.755519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:19.861544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:25.864038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:29.852301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:33.385171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:36.629420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:39.932484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:49.581530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:53.564741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:57.153149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:00.891697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:05.214098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:08.795065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:12.222640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:16.044794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:20.096922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:26.334555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:30.132610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:33.671996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:36.868029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:40.241640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:49.881476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:53.837671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:49:57.407575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:01.302855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:05.567709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:09.051813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:12.471445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-18T12:50:16.339909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2023-02-18T12:50:20.834995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-18T12:50:22.505595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexFlight DistanceDeparture Delay in MinutesArrival Delay in MinutesGender_FemaleGender_MaleCustomer Type_Loyal CustomerCustomer Type_disloyal CustomerType of Travel_Business travelType of Travel_Personal TravelClass_BusinessClass_EcoClass_Eco PlusAgeInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinesssatisfaction
006.1333983.2580972.9444390.01.01.00.00.01.00.00.01.0-1.7464683.04.03.01.05.03.05.05.04.03.04.04.05.05.0neutral or dissatisfied
115.4638320.6931471.945910.01.00.01.01.00.01.00.00.0-0.9519273.02.03.03.01.03.01.01.01.05.03.01.04.01.0neutral or dissatisfied
227.0414120.00.01.00.01.00.01.00.01.00.00.0-0.8857152.02.02.02.05.05.05.05.04.03.04.04.04.05.0satisfied
336.333282.4849072.3025851.00.01.00.01.00.01.00.00.0-0.9519272.05.05.05.02.02.02.02.02.05.03.01.04.02.0neutral or dissatisfied
445.3706380.00.00.01.01.00.01.00.01.00.00.01.4316973.03.03.03.04.05.05.03.03.04.04.03.03.03.0satisfied
557.0741170.00.01.00.01.00.00.01.00.01.00.0-0.8857153.04.02.01.01.02.01.01.03.04.04.04.04.01.0neutral or dissatisfied
667.1522692.3025853.1780540.01.01.00.00.01.00.01.00.00.5047322.04.02.03.02.02.02.02.03.03.04.03.05.02.0neutral or dissatisfied
777.6187421.6094380.01.00.01.00.01.00.01.00.00.00.8357914.03.04.04.05.05.05.05.05.05.05.04.05.04.0satisfied
886.7499310.00.01.00.01.00.01.00.01.00.00.00.1074621.02.02.02.04.03.03.01.01.02.01.04.01.02.0neutral or dissatisfied
996.9679090.00.00.01.00.01.01.00.00.01.00.0-1.2829853.03.03.04.02.03.03.02.02.03.04.04.03.02.0neutral or dissatisfied

Last rows

df_indexFlight DistanceDeparture Delay in MinutesArrival Delay in MinutesGender_FemaleGender_MaleCustomer Type_Loyal CustomerCustomer Type_disloyal CustomerType of Travel_Business travelType of Travel_Personal TravelClass_BusinessClass_EcoClass_Eco PlusAgeInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinesssatisfaction
1028151038946.5694812.8903723.2958370.01.01.00.01.00.01.00.00.0-0.8857154.04.04.04.05.05.05.05.03.04.04.03.04.05.0satisfied
1028161038956.9622432.6390572.3978951.00.00.01.01.00.00.01.00.0-1.0181381.01.01.02.01.01.01.01.03.03.05.05.04.01.0neutral or dissatisfied
1028171038966.7661920.00.00.01.01.00.01.00.00.01.00.01.166854.05.05.05.04.04.04.04.03.04.03.01.03.04.0neutral or dissatisfied
1028181038977.3777592.3025852.0794421.00.01.00.01.00.01.00.00.01.3654855.05.05.05.05.05.04.04.04.04.04.04.04.04.0satisfied
1028191038987.3907990.00.00.01.01.00.00.01.00.01.00.00.7033683.01.03.04.02.03.02.02.04.03.04.02.04.02.0neutral or dissatisfied
1028201038995.262691.3862940.01.00.00.01.01.00.00.01.00.0-1.084352.01.02.03.02.02.02.02.03.01.04.02.03.02.0neutral or dissatisfied
1028211039007.7613190.00.00.01.01.00.01.00.01.00.00.00.6371564.04.04.04.02.04.05.05.05.05.05.05.05.04.0satisfied
1028221039017.59892.0794422.708050.01.00.01.01.00.01.00.00.0-0.6208681.01.01.03.04.01.05.04.03.02.04.05.05.04.0neutral or dissatisfied
1028231039026.9087550.00.01.00.00.01.01.00.00.01.00.0-1.1505621.01.01.05.01.01.01.01.04.05.01.05.04.01.0neutral or dissatisfied
1028241039037.4524020.00.00.01.01.00.01.00.01.00.00.0-0.8195031.03.03.03.01.01.01.01.01.01.04.04.03.01.0neutral or dissatisfied